Jove
Visualize
Contact Us

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

106
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
106
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56
Causality in Epidemiology01:21

Causality in Epidemiology

302
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
302
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

475
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
475
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

301
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
301
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A scalable variational method for estimating the latent infection-rate field of an outbreak.

PloS one·2026
Same author

Detecting outbreaks using a spatial latent field.

PloS one·2025
Same author

Bayesian Calibration of Stochastic Agent Based Model via Random Forest.

Statistics in medicine·2025
Same author

Towards silent and efficient flight by combining bioinspired owl feather serrations with cicada wing geometry.

Nature communications·2024
Same author

Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference.

Sensors (Basel, Switzerland)·2023
Same author

A special issue on computational modeling and simulation of infectious diseases.

Computer methods in applied mechanics and engineering·2022
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 5, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Calibration verification for stochastic agent-based disease spread models.

Maya Horii1, Aidan Gould1, Zachary Yun1

  • 1Mechanical Engineering Department, University of California, Berkeley, Berkeley, California, United States of America.

Plos One
|December 10, 2024
PubMed
Summary
This summary is machine-generated.

Robust calibration verification is essential for reliable disease spread models. Simulation-based calibration using synthetic data helps identify challenges missed by standard validation, particularly with Bayesian inference and approximate Bayesian computation methods.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

3.4K

Related Experiment Videos

Last Updated: Jun 5, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

3.4K

Area of Science:

  • Epidemiology
  • Computational Biology
  • Statistical Modeling

Background:

  • Accurate disease spread modeling is vital for public health interventions.
  • Current calibration methods often lack independent verification, potentially masking errors.
  • Model validation alone may not fully assess calibration procedure reliability.

Purpose of the Study:

  • To develop and test a stochastic agent-based model for evaluating calibration techniques.
  • To compare a Bayesian inference method with a likelihood-free approximate Bayesian computation (ABC) approach.
  • To assess the utility of simulation-based calibration for verifying model calibration.

Main Methods:

  • Developed a stochastic agent-based disease spread model as a testing environment.
  • Employed simulation-based calibration using synthetic data for verification.
  • Implemented and compared a Bayesian inference method (with Markov chain Monte Carlo) and an ABC approach.

Main Results:

  • Simulation-based calibration revealed challenges with the empirical likelihood in the Bayesian method.
  • Approximate Bayesian computation (ABC) alleviated issues found with the Bayesian approach.
  • The Bayesian method performed adequately in standard synthetic data model validation tests.

Conclusions:

  • Stand-alone calibration verification using synthetic data is valuable for epidemiological research.
  • This approach can uncover calibration issues not apparent through standard model validation.
  • Simulation-based calibration offers a robust method for enhancing the reliability of disease spread models.